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# app.py
import gradio as gr
import fitz # PyMuPDF
import os
import requests
# --------------- GROQ GPT CALL ---------------
GROQ_API_KEY = os.getenv("GROQ_API_KEY") or "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"
GROQ_MODEL = "llama3-70b-8192"
def query_gpt(prompt):
headers = {
"Authorization": f"Bearer {GROQ_API_KEY}",
"Content-Type": "application/json"
}
data = {
"model": GROQ_MODEL,
"messages": [
{"role": "user", "content": prompt}
]
}
response = requests.post("https://api.groq.com/openai/v1/chat/completions", json=data, headers=headers)
return response.json()['choices'][0]['message']['content']
# --------------- PDF TEXT EXTRACTION ---------------
def extract_text_from_pdf(pdf_path):
doc = fitz.open(pdf_path)
text = ""
for page in doc:
text += page.get_text()
return text
# --------------- MAIN TASKS ---------------
def summarize_textbook(text):
prompt = f"Summarize the following content into important bullet points:\n\n{text}"
return query_gpt(prompt)
def generate_mcqs(text):
prompt = f"Generate 5 multiple choice questions (MCQs) with 4 options each from the following content:\n\n{text}"
return query_gpt(prompt)
def simplify_concepts(text):
prompt = f"Simplify and explain the following concepts for a student who is 14 years old:\n\n{text}"
return query_gpt(prompt)
def process_text_inputs(book, chapter, action_type):
user_prompt = f"Give a detailed explanation and key points for the chapter '{chapter}' from the book '{book}'"
if action_type == "Summarize Important Points":
return query_gpt(f"Summarize the following chapter:\n\n{user_prompt}")
elif action_type == "Generate MCQs":
return query_gpt(f"Generate 5 MCQs with 4 options each from:\n\n{user_prompt}")
elif action_type == "Simplify Concepts":
return query_gpt(f"Explain in simple terms the concepts from:\n\n{user_prompt}")
# --------------- GRADIO UI ---------------
with gr.Blocks(title="AI Textbook Tutor") as app:
gr.Markdown("# 📘 AI Textbook Tutor\nUpload your textbook or type a chapter and get summaries, MCQs, and simplified explanations!")
with gr.Tab("📄 Upload PDF"):
with gr.Row():
pdf_input = gr.File(label="Upload PDF", file_types=None)
action_pdf = gr.Radio([
"Summarize Important Points",
"Generate MCQs",
"Simplify Concepts"
], label="Select Task")
run_pdf = gr.Button("Run 🧠 on PDF")
output_pdf = gr.Textbox(label="📤 Output", lines=15)
with gr.Tab("🔍 Search by Book & Chapter"):
with gr.Row():
book_input = gr.Textbox(label="Book Name", placeholder="e.g., Physics 9th Class")
chapter_input = gr.Textbox(label="Chapter or Topic Name", placeholder="e.g., Measurement")
action_text = gr.Radio([
"Summarize Important Points",
"Generate MCQs",
"Simplify Concepts"
], label="Select Task")
run_text = gr.Button("Run 🧠 on Chapter")
output_text = gr.Textbox(label="📤 Output", lines=15)
def process_pdf(pdf_file, action_type):
text = extract_text_from_pdf(pdf_file)
if len(text) > 5000:
text = text[:5000]
if action_type == "Summarize Important Points":
return summarize_textbook(text)
elif action_type == "Generate MCQs":
return generate_mcqs(text)
elif action_type == "Simplify Concepts":
return simplify_concepts(text)
run_pdf.click(fn=process_pdf, inputs=[pdf_input, action_pdf], outputs=[output_pdf])
run_text.click(fn=process_text_inputs, inputs=[book_input, chapter_input, action_text], outputs=[output_text])
app.launch()
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